Cell-cell interaction analysis: liao et al.
Yingxin Lin
Last compiled on 24 December, 2020
1 Data and package
library(scran)
library(SingleCellExperiment)
library(scater)
library(scattermore)
library(moon)
library(ggplot2)
library(ggthemes)
library(ggpubr)
library(reshape2)
library(dplyr)
library(stringr)
library(pheatmap)
library(CellChat)
library(gridExtra)
library(RColorBrewer)meta_liao <- readRDS("results/liao_results/meta_liao.rds")
coldata_liao <- readRDS("results/liao_results/coldata_liao.rds")severity_color <- c("#2ca02c", "#FFD92F", "#7570B3")
names(severity_color) <- c("healthy control", "mild", "severe")# CCI results (Cellchat)
cellchat_res_list <- readRDS("results/liao_results/liao_cellchat_res_list.rds")2 Overall pattern analysis
rankNet_byCellType <- function(object, slot.name = "netP",
x.rotation = 90, title = NULL, color.use = NULL,
bar.w = 0.75, font.size = 8)
{
object1 <- methods::slot(object, slot.name)
prob1 = object1$prob
df <- melt(apply(prob1, 3, function(x) {
df <- melt(x)
colnames(df) <- c("Ligand", "Receptor", "value")
df
}))
df <- df[, c("Ligand", "Receptor", "L1", "value")]
colnames(df)[3] <- "Pathway"
return(df)
}rankNet_byCellType_list <- lapply(cellchat_res_list, rankNet_byCellType)
rankNet_byCellType_list <- melt(rankNet_byCellType_list)
rankNet_byCellType_list$Ligand_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Ligand),
"_"), "[[", 1))
rankNet_byCellType_list$Receptor_group <- unlist(lapply(strsplit(as.character(rankNet_byCellType_list$Receptor),
"_"), "[[", 1))
saveRDS(rankNet_byCellType_list, file = "results/liao_results/rankNet_byCellType_list_liao.rds")rankNet_byGroup_agg <- aggregate(rankNet_byCellType_list$value,
list(rankNet_byCellType_list$Ligand_group,
rankNet_byCellType_list$Receptor_group,
rankNet_byCellType_list$L1,
rankNet_byCellType_list$Pathway),
sum)
colnames(rankNet_byGroup_agg) <- c("Ligand_group",
"Receptor_group",
"sample",
"Pathway",
"value")
features <- paste(rankNet_byGroup_agg$Ligand_group,
rankNet_byGroup_agg$Receptor_group,
rankNet_byGroup_agg$Pathway, sep = "_")
rankNet_byGroup_agg$features <- featuresrankNet_byGroup_agg_all <- dcast2(rankNet_byGroup_agg,
features ~ sample,
fun.aggregate = sum, value.var = "value")
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all) > 0, ]
rankNet_byGroup_agg_all <- rankNet_byGroup_agg_all[rowSums(rankNet_byGroup_agg_all!=0) > 2, ]2.1 Feature selction: kruskal test
kruskal_pvalue <- list()
for (i in 1:nrow(rankNet_byGroup_agg_all)) {
#if (i %% 100 == 0) print(i)
kruskal_res <- try(kruskal.test(unlist(rankNet_byGroup_agg_all[i,]) ~ meta_liao[colnames(rankNet_byGroup_agg_all), ]$Condition2), silent = TRUE)
kruskal_pvalue[[i]] <- try(kruskal_res$p.value, silent = TRUE)
}
kruskal_pvalue <- lapply(kruskal_pvalue, function(x) {
if (class(x) == "try-error") {
x <- NULL
}
x
})
names(kruskal_pvalue) <- rownames(rankNet_byGroup_agg_all)
kruskal_pvalue <- unlist(kruskal_pvalue)
kruskal_pvalue <- p.adjust(kruskal_pvalue, method = "BH")
saveRDS(kruskal_pvalue, "results/liao_results/CCI_kruskal_pvalue_condition_liao.rds")2.2 PCA
pca_patient <- prcomp(t(-1/log(rankNet_byGroup_agg_all[names(kruskal_pvalue[kruskal_pvalue < 0.2]),])),
scale. = TRUE, center = TRUE)
library(ggrepel)
pca1 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 2],
color = meta_liao[rownames(pca_patient$x),]$Condition2)) +
geom_point(size = 4, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA2") +
labs(color = "")
pca2 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 3],
color = meta_liao[rownames(pca_patient$x),]$Condition2)) +
geom_point(size = 3, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA3") +
labs(color = "")
pca3 <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 2],
y = pca_patient$x[, 3],
color = meta_liao[rownames(pca_patient$x),]$Condition2)) +
geom_point(size = 3, alpha = 0.8) +
# geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_yx() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA2") +
ylab("PCA3") +
labs(color = "")
ggarrange(pca1, pca2, pca3, align = "hv",
common.legend = TRUE, ncol = 2, nrow = 2)
pca1_label <- ggplot(data.frame(pca_patient$x), aes(x = pca_patient$x[, 1],
y = pca_patient$x[, 2],
color = meta_liao[rownames(pca_patient$x),]$Condition2)) +
geom_point(size = 3, alpha = 0.8) +
geom_text_repel(aes(label = rownames(pca_patient$x))) +
theme_bw() +
theme(aspect.ratio = 1) +
scale_color_manual(values = severity_color) +
xlab("PCA1") +
ylab("PCA2") +
labs(color = "")2.3 Aggregation by samples
aff_mat_bySample <- lapply(split(rankNet_byGroup_agg, rankNet_byGroup_agg$sample),
function(x) dcast2(x, Ligand_group~Receptor_group,
fun.aggregate = mean, value.var = "value"))
all_cellTypes <- names(table(coldata_liao$pred_cellTypes_scClassify))
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
mat <- matrix(0, ncol = length(all_cellTypes), nrow = length(all_cellTypes))
colnames(mat) <- rownames(mat) <- all_cellTypes
mat[rownames(x), colnames(x)] <- as.matrix(x)
mat
})
aff_mat_bySample <- lapply(aff_mat_bySample, function(x) {
(x - min(x))/(max(x) - min(x))
})
p <- lapply(1:length(aff_mat_bySample), function(i) {
pheatmap(aff_mat_bySample[[i]],
cluster_cols = FALSE,
cluster_rows = FALSE,
main = names(aff_mat_bySample)[i],
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100))
})
pdf("figures/LiaoEtAl/cellchat_CCI_network_sample_byCellType.pdf",
width = 15, height = 10)
do.call(grid.arrange, list(grobs = lapply(p, function(x) x$gtable), ncol = 3))
dev.off()## quartz_off_screen
## 2#
severe_patients <- rownames(meta_liao)[meta_liao$Condition2 == "severe"]
aff_mat_severe <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% severe_patients])/length(severe_patients)
moderate_patients <- rownames(meta_liao)[meta_liao$Condition2 == "mild"]
aff_mat_moderate <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% moderate_patients])/length(moderate_patients)
control_patients <- rownames(meta_liao)[meta_liao$Condition2 == "healthy control"]
aff_mat_control <- Reduce("+", aff_mat_bySample[names(aff_mat_bySample) %in% control_patients])/length(control_patients)
p_severe <- pheatmap(aff_mat_severe, cluster_cols = FALSE,
cluster_rows = FALSE,
main = "severe (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_severe), max(aff_mat_severe)/100))
library(RColorBrewer)
p_moderate <- pheatmap(aff_mat_moderate,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "moderate (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_severe), max(aff_mat_severe)/100))
p_control <- pheatmap(aff_mat_control,
cluster_cols = FALSE,
cluster_rows = FALSE,
main = "control (average across samples)",
color = colorRampPalette(c("white",
brewer.pal(n = 7,
name = "Reds")))(100),
breaks = seq(0, max(aff_mat_control), max(aff_mat_control)/100))
pdf("figures/LiaoEtAl/cellchat_CCI_network_byCondition_noScale.pdf",
width = 12, height = 4)
do.call(grid.arrange, list(grobs = list(p_control$gtable,
p_moderate$gtable,
p_severe$gtable), ncol = 3))
dev.off()## quartz_off_screen
## 2aff_mat_diff <- aff_mat_severe - aff_mat_moderate
keep <- intersect(names(which(colSums(aff_mat_diff) != 0)),
names(which(rowSums(aff_mat_diff) != 0)))
pheatmap(aff_mat_diff[keep, keep],
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("blue", "white", "red"))(100)[c(seq(1, 35, 5),
36:100)],
main = "server - moderate (Liao et al.)",
#file = "figures/LiaoEtAl/cellchat_CCI_network_byCondition_diff_noScale.pdf",
width = 8,
height = 7)
3 Pathway-cluster cell-cell interaction
3.1 Monocyte -> Neutrophil
keep_Monocyte <- rankNet_byCellType_list$Receptor_group %in% "Neutrophil" &
rankNet_byCellType_list$Ligand_group %in% c("Monocyte")
pmat_Monocytes_neutrophil <- rankNet_byCellType_list[keep_Monocyte, ] %>%
dcast2(Pathway~L1,
fun.aggregate = sum, value.var = "value")
pmat_Monocytes_neutrophil <- pmat_Monocytes_neutrophil[rowSums(pmat_Monocytes_neutrophil) != 0 &
rowSums(pmat_Monocytes_neutrophil != 0) > 1, ]
chua_pathway_clust_monocytes <- readRDS("results/chua_results/cellchat_LigandMonocyte_ReceptorNeutrophils_pathway_cluster.rds")
anno_row <- data.frame(pathway_cluster = factor(chua_pathway_clust_monocytes))
rownames(anno_row) <- names(chua_pathway_clust_monocytes)
anno_color <- list()
anno_color$pathway_cluster <- RColorBrewer::brewer.pal(length(table(anno_row)), "Set2")
names(anno_color$pathway_cluster) <- seq_len(length(table(anno_row)))
pmat_Monocytes_neutrophil <- pmat_Monocytes_neutrophil[rownames(pmat_Monocytes_neutrophil) %in% names(chua_pathway_clust_monocytes),]
chua_pathway_clust_monocytes <- chua_pathway_clust_monocytes[rownames(pmat_Monocytes_neutrophil)]
pmat_Monocytes_neutrophil <- pmat_Monocytes_neutrophil[names(chua_pathway_clust_monocytes)[order(chua_pathway_clust_monocytes, rowMeans(pmat_Monocytes_neutrophil))],]
pheatmap(-1/log(pmat_Monocytes_neutrophil),
#annotation_col = anno_col,
annotation_colors = anno_color,
annotation_row = anno_row,
clustering_method = "ward.D2",
cluster_cols = FALSE,
cluster_rows = FALSE,
color = colorRampPalette(c("white",
brewer.pal(n = 9, name = "Reds")))(100),
main = "Ligand: Monocytes; Recetpor: Neutrophils (Liao et al.)",
#file = "figures/LiaoEtAl/cellchat_LigandMonocyte_ReceptorNeutrophils_heatmap.pdf",
height = 12,
width = 6
)
4 Session Info
sessionInfo()## R version 4.0.2 RC (2020-06-20 r78727)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_AU.UTF-8/en_AU.UTF-8/en_AU.UTF-8/C/en_AU.UTF-8/en_AU.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggrepel_0.8.2 RColorBrewer_1.1-2
## [3] gridExtra_2.3 CellChat_0.0.1
## [5] bigmemory_4.5.36 pheatmap_1.0.12
## [7] stringr_1.4.0 dplyr_1.0.2
## [9] reshape2_1.4.4 ggpubr_0.3.0
## [11] ggthemes_4.2.0 moon_0.1.0
## [13] scattermore_0.6 scater_1.16.1
## [15] ggplot2_3.3.2 scran_1.16.0
## [17] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
## [19] DelayedArray_0.14.0 matrixStats_0.56.0
## [21] Biobase_2.48.0 GenomicRanges_1.40.0
## [23] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [25] S4Vectors_0.26.1 BiocGenerics_0.34.0
##
## loaded via a namespace (and not attached):
## [1] readxl_1.3.1 backports_1.1.8
## [3] circlize_0.4.10 systemfonts_0.2.3
## [5] NMF_0.30.1 plyr_1.8.6
## [7] igraph_1.1.0 BiocParallel_1.22.0
## [9] listenv_0.8.0 gridBase_0.4-7
## [11] digest_0.6.25 foreach_1.5.0
## [13] htmltools_0.5.0 viridis_0.5.1
## [15] ggalluvial_0.12.0 magrittr_1.5
## [17] cluster_2.1.0 doParallel_1.0.15
## [19] openxlsx_4.1.5 limma_3.44.3
## [21] sna_2.5 ComplexHeatmap_2.4.2
## [23] globals_0.12.5 svglite_1.2.3.2
## [25] colorspace_1.4-1 haven_2.3.1
## [27] xfun_0.18 crayon_1.3.4
## [29] RCurl_1.98-1.2 jsonlite_1.6.1
## [31] bigmemory.sri_0.1.3 iterators_1.0.12
## [33] glue_1.4.1 registry_0.5-1
## [35] gtable_0.3.0 zlibbioc_1.34.0
## [37] XVector_0.28.0 GetoptLong_1.0.0
## [39] car_3.0-8 BiocSingular_1.4.0
## [41] future.apply_1.5.0 shape_1.4.4
## [43] abind_1.4-5 scales_1.1.1
## [45] edgeR_3.30.3 rngtools_1.5
## [47] bibtex_0.4.2.2 rstatix_0.6.0
## [49] Rcpp_1.0.4.6 viridisLite_0.3.0
## [51] xtable_1.8-4 clue_0.3-57
## [53] reticulate_1.16 dqrng_0.2.1
## [55] foreign_0.8-80 rsvd_1.0.3
## [57] FNN_1.1.3 ellipsis_0.3.1
## [59] farver_2.0.3 pkgconfig_2.0.3
## [61] locfit_1.5-9.4 labeling_0.3
## [63] tidyselect_1.1.0 rlang_0.4.9
## [65] munsell_0.5.0 cellranger_1.1.0
## [67] tools_4.0.2 generics_0.0.2
## [69] statnet.common_4.3.0 broom_0.7.2
## [71] evaluate_0.14 yaml_2.2.1
## [73] knitr_1.30 zip_2.0.4
## [75] purrr_0.3.4 dendextend_1.13.4
## [77] pbapply_1.4-2 future_1.17.0
## [79] compiler_4.0.2 beeswarm_0.2.3
## [81] curl_4.3 png_0.1-7
## [83] ggsignif_0.6.0 tibble_3.0.4
## [85] statmod_1.4.34 stringi_1.4.6
## [87] RSpectra_0.16-0 gdtools_0.2.2
## [89] forcats_0.5.0 lattice_0.20-41
## [91] Matrix_1.2-18 vctrs_0.3.5
## [93] pillar_1.4.4 lifecycle_0.2.0
## [95] GlobalOptions_0.1.2 BiocNeighbors_1.6.0
## [97] data.table_1.12.8 cowplot_1.0.0
## [99] bitops_1.0-6 irlba_2.3.3
## [101] R6_2.4.1 network_1.16.0
## [103] rio_0.5.16 vipor_0.4.5
## [105] codetools_0.2-16 assertthat_0.2.1
## [107] pkgmaker_0.31.1 rjson_0.2.20
## [109] withr_2.2.0 GenomeInfoDbData_1.2.3
## [111] hms_0.5.3 grid_4.0.2
## [113] coda_0.19-3 tidyr_1.1.2
## [115] rmarkdown_2.4 DelayedMatrixStats_1.10.0
## [117] carData_3.0-4 ggbeeswarm_0.6.0